LightGBMClassifier

class LightGBMClassifier.LightGBMClassificationModel(java_model=None)

Bases: mmlspark._LightGBMClassifier.M

getFeatureImportances(importance_type='split')

Get the feature importances. The importance_type can be “split” or “gain”.

saveNativeModel(sparkSession, filename)

Save the booster as string format to a local or WASB remote location.

class LightGBMClassifier.LightGBMClassifier(baggingFraction=1.0, baggingFreq=0, baggingSeed=3, defaultListenPort=12400, earlyStoppingRound=0, featureFraction=1.0, featuresCol='features', labelCol='label', learningRate=0.1, maxBin=255, maxDepth=-1, minSumHessianInLeaf=0.001, numIterations=100, numLeaves=31, objective='regression', parallelism='data_parallel', predictionCol='prediction', probabilityCol='probability', rawPredictionCol='rawPrediction', thresholds=None, timeout=120.0)

Bases: mmlspark._LightGBMClassifier._LightGBMClassifier